Does it support JSONB performance?

Yes, it includes specific implementation patterns for GIN indexes and optimized JSONB operator usage to ensure efficient document storage and retrieval.

Does it help with database connection issues?

It includes best practices and configuration patterns for connection pooling using PgBouncer to reduce server memory overhead and improve high-concurrency handling.

How does this skill help with slow queries?

It provides templates for interpreting EXPLAIN ANALYZE results and suggests specific indexing strategies, such as composite or partial indexes, to eliminate costly sequence scans.

When should I use the partitioning guidelines?

The skill recommends considering partitioning for tables exceeding 10 million rows where queries consistently filter by specific keys, such as date ranges or regions.

Can this skill identify unused indexes?

Yes, it provides SQL snippets to query system statistics like pg_stat_user_indexes to find and remove redundant indexes that are slowing down write operations.

PostgreSQL Optimization

Name: PostgreSQL Optimization
Author: micsapp

bymicsapp

0•

Gestión de Bases de Datos

Optimizes PostgreSQL database performance through advanced indexing, query plan analysis, partitioning, and connection pooling.

This skill equips Claude with specialized knowledge for fine-tuning PostgreSQL databases to handle high loads and complex queries. It provides implementation patterns for various indexing strategies (including GIN and GiST), instructions for interpreting query plans using EXPLAIN ANALYZE, and configurations for connection pooling with PgBouncer. Whether you're dealing with slow-performing JSONB operations or tables exceeding 10 million rows that require partitioning, this toolkit offers the domain-specific guidance needed to eliminate bottlenecks and ensure database scalability.

Características Principales

01In-depth query plan analysis using EXPLAIN ANALYZE to identify sequence scans and bottlenecks.

02Implementation of advanced indexing strategies like partial, composite, and covering indexes.

030 GitHub stars

04Performance-oriented JSONB query and update optimization techniques.

05Table partitioning patterns for managing large-scale datasets efficiently.

06Connection pooling configuration and server tuning parameters for high-concurrency environments.

Casos de Uso

01Designing a scalable database schema for high-growth applications with millions of rows.

02Optimizing database connection management and server resource utilization for high-traffic apps.

03Troubleshooting and fixing slow-running SQL queries in a production environment.

Características Principales

01In-depth query plan analysis using EXPLAIN ANALYZE to identify sequence scans and bottlenecks.

02Implementation of advanced indexing strategies like partial, composite, and covering indexes.

030 GitHub stars

04Performance-oriented JSONB query and update optimization techniques.

05Table partitioning patterns for managing large-scale datasets efficiently.

06Connection pooling configuration and server tuning parameters for high-concurrency environments.

Casos de Uso

01Designing a scalable database schema for high-growth applications with millions of rows.

02Optimizing database connection management and server resource utilization for high-traffic apps.

03Troubleshooting and fixing slow-running SQL queries in a production environment.